Cardiometabolic-based chronic disease: adiposity and dysglycemia drivers of heart failure

Correia, Eduardo Thadeu de Oliveira; Mechanick, Jeffrey I.; Barbetta, Letícia Mara dos Santos; Jorge, Antônio José Lagoeiro; Mesquita, Evandro Tinoco

doi:10.1007/s10741-022-10233-x

Cited by 10 publications

(8 citation statements)

References 103 publications

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“…However, these studies tend to focus solely on the general clinical characteristics of patients. 25 , 26 Notably, the development of postoperative myocardial injury can be influenced by the neurological and endocrine systems, as alterations in these systems may cause overexpression of proinflammatory cytokines and signaling molecules. Thus, the present study considered relevant inflammatory indicators to assess their impact on the development of myocardial injury.…”

Section: Discussionmentioning

confidence: 99%

Identification of High-Risk Patients for Postoperative Myocardial Injury After CME Using Machine Learning: A 10-Year Multicenter Retrospective Study

Liu¹,

Chen²,

Tian³

et al. 2023

IJGM

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Purpose The occurrence of myocardial injury, a grave complication post complete mesocolic excision (CME), profoundly impacts the immediate and long-term prognosis of patients. The aim of this inquiry was to conceive a machine learning model that can recognize preoperative, intraoperative and postoperative high-risk factors and predict the onset of myocardial injury following CME. Patients and Methods This study included 1198 colon cancer patients, 133 of whom experienced myocardial injury after surgery. Thirty-six distinct variables were gathered, encompassing patient demographics, medical history, preoperative examination characteristics, surgery type, and intraoperative details. Four machine learning algorithms, namely, extreme gradient boosting (XGBoost), random forest (RF), multilayer perceptron (MLP), and k-nearest neighbor algorithm (KNN), were employed to fabricate the model, and k-fold cross-validation, ROC curve, calibration curve, decision curve analysis (DCA), and external validation were employed to evaluate it. Results Out of the four predictive models employed, the XGBoost algorithm demonstrated the best performance. The ROC curve findings indicated that the XGBoost model exhibited remarkable predictive accuracy, with an area under the curve (AUC) value of 0.997 in the training set and 0.956 in the validation set. For internal validation, the k-fold cross-validation method was utilized, and the XGBoost model was shown to be steady. Furthermore, the calibration curves demonstrated the XGBoost model’s high predictive capability. The DCA curve revealed higher benefit rates for patients who underwent interventional treatment under the XGBoost model. The AUC value for the external validation set was 0.74, which indicated that the XGBoost prediction model possessed good extrapolative capacity. Conclusion The myocardial injury prediction model for patients undergoing CME that was developed using the XGBoost machine learning algorithm in this study demonstrates both high predictive accuracy and clinical utility.

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Section: Discussionmentioning

confidence: 99%

Identification of High-Risk Patients for Postoperative Myocardial Injury After CME Using Machine Learning: A 10-Year Multicenter Retrospective Study

Liu¹,

Chen²,

Tian³

et al. 2023

IJGM

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“…Each condition was coded as a dichotomous variable, with the presence of disease = 1 [ 12 ]. Obesity and type 2 diabetes are major cardiometabolic drivers, represented as distinct stages of ABCD and DBCD, respectively, and leading to CMBCD [ 14 ]. CMBCD was defined as the presence of at least one of the heart disease, stroke, diabetes, hypertension, and dyslipidemia in a single individual.…”

Section: Methodsmentioning

confidence: 99%

Development and external validation of a diagnostic model for cardiometabolic-based chronic disease : results from the China health and retirement longitudinal study (CHARLS)

2023

BMC Cardiovasc Disord

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Background Cardiovascular disease(CVD) is the leading cause of death in the world. Cardiometabolic-based chronic disease (CMBCD) model is presented that provides a basis for sustainable and early, evidence-based therapeutic targeting to mitigate the ravagest and development of CVD. CMBCD include dysglycemia, hypertension, and/or dyslipidemia progressing to downstream CVD events. Objectives The objective of our research was to develop and externally validate a diagnostic model of CMBCD. Methods Design: Multivariable logistic regression of a cohort for 9,463 participants aged at least 45 years were drawn from the 2018 wave of the China Health and Retirement Longitudinal Study (CHARLS). Setting: The 2018 wave of the CHARLS. Participants:Diagnostic model development: Totally 6,218 participants whose individual ID < 250,000,000,000. External validation: Totally 3,245 participants whose individual ID > 250,000,000,000. Outcomes: CMBCD . Results CMBCD occurred in 25.5%(1,584/6,218)of individuals in the development data set and 26.2%(850 /3,245)of individuals in the validation data set. The strongest predictors of CMBCD were age, general health status, location of residential address, smoking, housework ability, pain, and exercise tolerance. We developed a diagnostic model of CMBCD. Discrimination was the ability of the diagnostic model to differentiate between people who with and without CMBCD. This measure was quantified by calculating the area under the receiver operating characteristic(ROC) curve(AUC).The AUC was 0.6199 ± 0.0083, 95% confidence interval(CI) = 0.60372 ~ 0.63612. We constructed a nomograms using the development database based on age, general health status, location of residential address, smoking, housework ability, pain, and exercise tolerance. The AUC was 0.6033 ± 0.0116, 95% CI = 0.58066 ~ 0.62603 in the validation data set. Conclusions We developed and externally validated a diagnostic model of CMBCD. Discrimination, calibration, and decision curve analysis were satisfactory.

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“…The second dimension is based on a systems/networking effect of primary (genetics, environment, and behavior; together yielding a personalized lifestyle) and secondary/metabolic drivers (abnormal adiposity, dysglycemia, HTN, and dyslipidemia) [ 28 ]. The four secondary/metabolic drivers are interpreted individually according to the aforementioned stages as: adiposity-based chronic disease (ABCD) [ 29 ], dysglycemia-based chronic disease (DBCD) [ 30 ], hypertension-based chronic disease (HBCD), and lipid-based chronic disease (LBCD) [ 31 ] culminating in a final driver pathway, cardiovascular disease (e.g., atherosclerosis, heart failure, and atrial fibrillation)—a total of five cardiometabolic drivers. In these driver-based chronic disease models, obesity is ABCD stage 3, T2D is DBCD stage 3, and HTN is HBCD stage 3.…”

Section: The Importance Of Culturally Adapted Interventions For Migra...mentioning

confidence: 99%

Telehealth and cardiometabolic-based chronic disease: optimizing preventive care in forcibly displaced migrant populations

Nieto-Martínez,

De Oliveira-Gomes,

Gonzalez-Rivas

et al. 2023

J Health Popul Nutr

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The number of migrants, which includes forcibly displaced refugees, asylum seekers, and undocumented persons, is increasing worldwide. The global migrant population is heterogeneous in terms of medical conditions and vulnerability resulting from non-optimal metabolic risk factors in the country of origin (e.g., abnormal adiposity, dysglycemia, hypertension, and dyslipidemia), adverse travel conditions and the resulting stress, poverty, and anxiety, and varying effects of acculturation and access to healthcare services in the country of destination. Therefore, many of these migrants develop a high risk for cardiovascular disease and face the significant challenge of overcoming economic and health system barriers to accessing quality healthcare. In the host countries, healthcare professionals experience difficulties providing care to migrants, including cultural and language barriers, and limited institutional capacities, especially for those with non-legal status. Telehealth is an effective strategy to mitigate cardiometabolic risk factors primarily by promoting healthy lifestyle changes and pharmacotherapeutic adjustments. In this descriptive review, the role of telehealth in preventing the development and progression of cardiometabolic disease is explored with a specific focus on type 2 diabetes and hypertension in forcibly displaced migrants. Until now, there are few studies showing that culturally adapted telehealth services can decrease the burden of T2D and HTN. Despite study limitations, telehealth outcomes are comparable to those of traditional health care with the advantages of having better accessibility for difficult-to-reach populations such as forcibly displaced migrants and reducing healthcare associated costs. More prospective studies implementing telemedicine strategies to treat cardiometabolic disease burden in migrant populations are needed.

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Cardiometabolic-based chronic disease: adiposity and dysglycemia drivers of heart failure

Cited by 10 publications

References 103 publications

Identification of High-Risk Patients for Postoperative Myocardial Injury After CME Using Machine Learning: A 10-Year Multicenter Retrospective Study

Identification of High-Risk Patients for Postoperative Myocardial Injury After CME Using Machine Learning: A 10-Year Multicenter Retrospective Study

Development and external validation of a diagnostic model for cardiometabolic-based chronic disease : results from the China health and retirement longitudinal study (CHARLS)

Telehealth and cardiometabolic-based chronic disease: optimizing preventive care in forcibly displaced migrant populations

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